1 Field of the Invention
This invention relates generally to online analytical processing (OLAP) systems and more particularly to model based optimization with focus regions in an OLAP system.
2 Description of the Related Art
Online analytical processing (OLAP) systems are typically used to access and analyze business data. Business data typically comprises sales, product and financial data over various time periods. Using an OLAP system, an analyst can explore business results interactively. A dimension is a collection of related attributes of the data values of the OLAP system, for example, product, market, time, channel, scenario and customer. OLAP systems are typically multidimensional. To understand their businesses, business analysts frequently work with data which is aggregated across various business dimensions. This provides analysts with the ability to explore business information in context, for example, sales by product by customer by time, or defects by manufacturing plant by time.
In an OLAP system, dimensional models allow business analysts to interactively explore information across multiple viewpoints at multiple levels of aggregation, also referred to as levels. A dimension typically comprises many levels, and the levels are typically hierarchical. The business data is typically aggregated across various dimensions at various levels to provide different views of the data at different levels of aggregation. The data may be aggregated over various periods of time, by geography, by teams and by product, depending on the type and organization of the business. Aggregated data is commonly referred to as an aggregation. For example, an aggregation may comprise the sales data for the month of July for a specified product.
Business analysts issue queries to retrieve business data and may request aggregations at various levels. If the aggregations are not available, then the aggregations will be computed. Computing aggregations can take a large amount of time and query processing may be slow.
Various OLAP systems store pre-computed aggregations to improve the performance of query processing. However, OLAP systems typically have a limited amount of storage space and cannot store all possible aggregations.
Some OLAP systems employ performance advisors to make recommendations as to what aggregations to pre-compute for improving the performance of queries. Model based performance advisors make the recommendations based on meta-data that describes the system. Workload-based performance advisors make recommendations based on query workloads that are provided.
One advantage of a model based performance advisor is the ability to make recommendations at the time the model is defined rather than waiting for a history of queries to develop. In addition, optimization can be performed for the complete model rather than incrementally changing the recommendations as query workload histories are developed. If the model is extended, that is, if the model is changed, a model based performance advisor can immediately make appropriate recommendations to reflect the changes.
Workload-based performance advisors typically require that users provide query workloads that represent the overall system usage. In practice, this can be difficult. Even if the query workload is generated by capturing the actual queries being executed, there is a risk that a new pattern of queries will develop. The new pattern of queries may result from a change to the meta-data model or from a change in usage because the needs of the business changed. For a workload-based performance advisor, the user typically waits for a new query history to develop before appropriate recommendations can be made to reflect the changes. One advantage of workload-based performance advisors is that the workload-based performance advisors will only optimize the portion of the model that is actually being used.
One disadvantage to the model based performance advisor is that it may not be practical to optimize for the entire model. For large, complex models, it can be prohibitively expensive to optimize the entire model.
Therefore, there is a need for a technique to provide an improved model based performance advisor. This technique should allow the model based performance advisor to optimize portions of the model.